code: def load_glove_model(filename, embed_dim):
      if os.path.exists(filename + ".cache"):
          logger.info("Found cache. Loading...")
          with open(filename + ".cache", "rb") as fp:
              return pickle.load(fp)
      embedding = {"mapping": dict(), "pool": []}
      with open(filename) as f:
          for i, line in enumerate(f):
              line = line.rstrip("\n")
              vocab_word, *vec = line.rsplit(" ", maxsplit=embed_dim)
              assert len(vec) == 300, "Unexpected line: '%s'" % line
              embedding["pool"].append(
                  np.array(list(map(float, vec)), dtype=np.float32)
              )
              embedding["mapping"][vocab_word] = i
      embedding["pool"] = np.stack(embedding["pool"])
      with open(filename + ".cache", "wb") as fp:
          pickle.dump(embedding, fp)
      return embedding
  
imports: import logging
  import os
  import pickle
  from collections import Counter
  import numpy
  import torch
  from torch.utils import data
label_desc: Use image node_id 11 for calling a global function with example usage: load_glove_model(filename, embed_dim) and returns: embedding, pickle